collective work in dynamic inter-organizational scheduling - CiteSeerX

Proceedings of the Fifth Asia Pacific Industrial Engineering and Management Systems Conference 2004

COLLECTIVE WORK IN DYNAMIC INTER-ORGANIZATIONAL SCHEDULING Kaveh Nezamirad*, Peter G Higgins* and Simon Dunstall** *Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, PO Box 218, Hawthorn 3122, Australia ** CSIRO Mathematical and Information Sciences, Private Bag 10, South Clayton MDC 3169, Australia {knezamirad, phiggins}@swin.edu.au, [email protected]

ABSTRACT Inter-organizational scheduling is a process, where two or more organizations coordinate activities for mutual benefits. Decision making in such an environment is a multi-criteria, multi-party practice, including cooperation between parties. It is characterized by distributed, dynamic, ill-defined and conflicting information. As this information is in the form of tacit knowledge, its efficient transference between organizations is not possible through databases and computer-supported tools. Therefore human collective work remains a key factor in interorganizational scheduling. This, comprising interaction between human operators, algorithms, software, and autonomous agents implies a need for structural and functional concepts. In this paper, inter-organizational dynamic collective work is studied using a cognitive-based analysis. Our aim is to identify the key factors affecting the process. Through a comparative review of the literature, it is argued that cooperative processes, involving coordination mechanisms, are one component of collaborative states in collective works in scheduling between organizations. In such a way, in collaborative scheduling, group and domain knowledge and tasks, group knowledge, group decision processes, and cooperative activities play a key role. This approach can contribute in system analysis, (re) design, and evaluation as well as designing computer supports in inter-organizational scheduling. Key Words: Scheduling, DSS and Expert Systems, Cognitive Human Factors

1. INTRODUCTION Global business and industrial requirements today compel organizations to coordinate their activities in common circumstances using the resources and capabilities of other organizations to achieve mutual goals. Inter-organizational scheduling is a group effort in which schedulers from different organizations aim to schedule both individual and joint tasks. Individual problem-solving, coupled with a participatory role, helps to ensure ownership and understanding of group decisions and hence, increases likelihood of successful schedule execution. To do this, participating organizations exchange information and cooperate with each other. Human schedulers have a crucial effect, as some information exchange between organizations depends on the context, which cannot be properly dealt with by computerized tools. This paper is a study of the collective work in scheduling between organizations. It begins with a review of literature with emphasize on the lack of explicit definitions of attributes of inter-organizational scheduling. A cognitive analysis of human cooperation is then presented

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followed by a discussion of the relation between cooperation and collaboration in collective work. It includes issues associated with the division of labour and coordination that lead to cooperation processes being considered as a component of collaborative states. Finally, we discuss the usefulness in perceiving inter-organizational scheduling in terms of collaborative state and cooperative processes. Implication of this approach is useful to design, analysis, and evaluation of scheduling functions and computer supports. 2. INTER-ORGANIZATIONAL SCHEDULING As manufacturing techniques evolved, industries minimised material wastage and optimised material flow through evolutionary waves of concepts applied to manufacturing such as MRP, agile manufacturing and JIT (McClellan, 2003). Following these advances, information associated with material flow became the next bottleneck that had to be addressed. Since the 1980s, there has been a focus by researchers and practitioners on inter-organizational scheduling as a form of information exchange between organizations, which has been stimulated by the revolutionary changes in computer science and hardware. New studies of material flow in manufacturing started in the 1950s, when MRP was introduced. After that, ‘agile manufacturing’ was used first in the late 1970s to describe philosophical less rigid approaches that were more focussed on the customer’s needs. Extending involvement in decision making to all the stakeholders in manufacturing processes occurred in 1980s. In the early 1990s this led to lean manufacturing, in which production is ‘pulled’ by customer demands. To control production, these new approaches require a profusion of information that has to be gathered, stored and analysed. For example, McKay (2003) addresses modelling of work flow by integrated systems of information. He refers to material masters, production orders, and inventory records in MRP systems that track material usage and availability. In this new era, organizational interchange, real facts, and shared information are the basis for decisionmaking processes. From the late 1990s, many researchers have confronted new challenges in business partnerships, from individual to cooperative and then ‘collaborative’ arrangements based on mutual benefits: Harvey (2001) and Hammond et al. (2001) in manufacturing; McClellan (2003) and Seifert (2002) in supply chain management, and Shen (2002) in artificial intelligence. Collaboration between distinct production units may involve automating, linking, complementing, or supporting business processes across departmental, plant, enterprise, or supply chain boundaries (McClellan 2003). Coordination of the activities of the collaborating parties becomes important as their business activities become entwined, with real-time exchange of information about goals, tasks, and resources. The planning of joint activities becomes important in many areas, such as demand planning, distribution planning, constraint-based master planning, transportation planning, and manufacturing planning and scheduling (Seifert, 2002). To coordinate their activities for mutual benefits, collaborating groups need to develop schedules together. Collective work on inter-organizational scheduling might occur between organizations that differ in size, domain expertise, geographical location, business objectives or facilities. Planning and scheduling in such an environment is a multi-attribute, multicriteria, multi-party task, involving optimisation of both individual and joint activities associated with resource allocation. The many factors affecting inter-organizational planning and scheduling have been studied from different perspectives in operational research, artificial intelligence, supply chain management, computer-supported cooperative work and cognitive engineering. These studies are addressed in the literature under different titles including distributed, collaborative, cooperative, and coordinated scheduling (Chan et al., 1999; Mokhtar et al., 2000; Gaonkar and Viswanadham, 2001; Shen, 2002; Seifert, 2002; Nezamirad et al., 2004; Kowalczyk et al., 2004).

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Like some new overwhelming terms in manufacturing — Globalization, virtual organizations, electronically-connected supply networks, which are used without having a consensus made between practitioners and researchers on unique definition of them — terms applied to inter-organizational planning and scheduling lack commonly agreed meaning. There is a spate of definitions for cooperative, collaborative, and coordinated scheduling, each with something to offer and none being entirely satisfactory. A generally applicable model of inter-organizational scheduling is needed, that has clearly defined concepts and observable entities. In investigating an exhaustive definition for collaboration, Wood and Gray (1991) stress that: “definitions are crucial to theory building”. When modelling teams in manufacturing, Buzacott (2004) pointed out that “once work is divided up then it has to be coordinated as the job or project is not complete until all tasks are complete. So it is useful to develop some models that provide insight into the impact of this division of labour and coordination. In particular, as a base case it is useful to model the performance of systems where the task division is in advance of beginning the project or job.” Today’s research focus on collective work in inter-organizational scheduling requires deeper conceptual modelling. By applying the concepts of ‘group knowledge and information sharing’, organizational description can be extended beyond transactional relationships that do not support synchronized planning. Without ‘group roles’ and ‘group goals’, only information-sharing relationships exist, which support synchronized but independent planning (Nokkentved, 2000). By stressing ‘collaborative’ relationships, the model by the authors referred to in this paper includes shared meaning: “… collaboration creates a shared meaning … something is there that wasn’t there before” (Denise, 1999). It allows for the inclusion of social aspects of group work, discussed by Harvey and Koubek (2000): cooperative group learning and problem-solving; collective inductions; collaborative results that are more than the individuals members could learn. However, Harvey and Koubek lack rigour in applying these terms; they use cooperative and collaborative learning interchangeably. While they refer to ‘common ground’ or ‘group vocabulary schema’ as a part of collaborative work, their work lacks a common vocabulary and definitions. Inter-organizational scheduling is a complicated activity involving complex network of goals, some of which may be unstated, for which optimum solutions are generally elusive. Moreover, heuristics and rule-based approaches might not produce realistic solutions, because they inadequately represent the environment due to insufficient information. Information may be insufficiently captured, where it is distributed, ill defined, uncertain and conflicting. It may also depend on understanding context from within a particular party’s domain. It may therefore be difficult to represent in a computerized decision support system (Nezamirad et al., 2004). Scheduling processes that involve joint activities across parties add a further dimension of difficulty, as they have to deal with changing events and circumstances within each participant’s domain. In such environments, human experts are needed to address the problematic issues that cannot be properly handled by software agents (McKay et al., 1988; Higgins, 1999; Wiers, 1997; Sanderson, 1989). Therefore, human schedulers have a key role and remain crucial in inter-organizational scheduling. Inter-organizational scheduling requires human experts to exchange information and to solve problems together. Working on scheduling tasks as a group, they try to enhance performance of the group by helping each other to facilitate their tasks and goals. This cooperative activity is a key concept in group work. Human cooperation is integral to interorganizational scheduling. The following section contains a brief discussion of the concepts related to cooperation and its attributes and applications.

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3. HUMAN COOPERATION Cooperation has been discussed by many researchers from a wide range of expertise. Panitz (1997) working in the field of interactive learning defines cooperation as a structure of interaction designed to facilitate the accomplishment of an end goal. He stresses structure in cooperation. Dix (1994) sees cooperation from a Human Computer Interaction (HCI) perspective as communication with a purpose. Castelfranchi (1998), from an AI perspective, refers to cooperation as a function of mutual dependence. While the stress varies between definitions, they have as a common foundation the concepts of interaction and dependence. As all these variants in definition are relevant to scheduling between organizations, they can be included in a comprehensive definition of cooperation. Hoc’s definition of cooperation as the management of interferences can be usefully applied to inter-organizational scheduling (Hoc, 2001). He describes cooperative work as a sub-class of collective work in which participants try to manage interference in real time, without having necessarily a common regulating goal. He expresses cooperation in two dimensions. Firstly, each party strives towards goals and can interfere with others on goals, resources, and procedures. Secondly, each party tries to facilitate the individual activities and/or the joint task through managing interferences. Interference caused by participants can be distinguished by four major types of precondition, interaction, mutual control, and redundancy, where they could be negative or positive. For the first form, one party’s activity can be a precondition for other’s activity. Intertwined project scheduling is an example of this category; time estimation of the whole project is dependent on time estimation in each sub-project. In the second form, parties depend on each other to achieve their goals. For instance, a scheduler changing his/her own organization’s schedule may affect another scheduler’s current activities and resources. Mutual control concerns reliability issues in control domains, where more than one party control the same activity. Managerial controls in preparing schedules in group are examples of this type of interferences. The last form concerns adaptive ability in novel situations where more than one party are potential doers of a single task. Following this definition of cooperation, Nezamirad et al. (2004) refer to three characteristics for cooperative activities. They refer to structures, levels, and forms of cooperative activities based on the literature. Hoc classifies cooperative work into three levels of abstraction: action, plan, and meta levels. The action level contains activities associated with local interference management. It comprises creation, detection, and resolution of local interference. Examples of action-level cooperative scheduling are detailed feasibility investigations of schedules and their visualisation based on domain knowledge. The concerns of the plan level are the creation and maintenance of group knowledge, which Hoc calls the maintenance of a common frame of reference (Hoc, 2001). The act of defining work organization and sharing domain-based information, such as available resources, are examples of this. At the meta level, the concern of cooperative work is the integration of long-term schedule construction, where high-level abstraction activities such as communication code generation and producing compatible reports are addressed. Another characteristic of cooperative work is its form. Schmidt (1991) suggests three forms of such collective work: augmentative, integrative, and debative. In augmentative form of cooperation, similar subtasks of the joint task are shared between members having similardomain-knowledge; all members do the same activity to reduce their workload. Schedulers are expected to have similar contextual information and cooperate at comparable levels of responsibility and authority. In the integrative form, participants with different domain knowledge cooperate over different and complementary subtasks of a joint task. Schedulers cooperate in order to integrate contextual information at different levels of abstraction. In

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contrast, the debative form occurs when participants, cooperate over all subtasks; together they compare, debate and correct issues relating to each subtask. Cooperative scheduling is a type of collective work that can be described using various structural frameworks. The structure of the work is highly correlated to both the group roles and schedulers’ expertise and authority. Following the theoretical constructs of Millot and Lemoine (1998), cooperation can be structured using two generic forms: vertical and horizontal. In the vertical form, differences between parties regarding authority and responsibility cause a hierarchical relationship. A necessary condition in the managing of interference and goal facilitation is that the higher-level party calls upon the other at lower the level to cooperate on a task. However, when all parties in are working at the same horizontal structural level, a heterarchical relationship occurs between them. They share responsibility based on their domain authority. 4. COLLABORATION AND COOPERATION IN COLLECTIVE WORK Some researchers treat ‘cooperation’ and ‘collaboration’ as synonymous, while other researcher across a broad spectrum of disciplines treat them as distinctly different (Dillenbourg et al., 1996). In surveying the literature, we found two attributes that can be used to distinguish these terms: ‘division of labour’ and ‘coordination’ (see also Brna, 1998). However, we argue here that these attributes are not suitable to demark the difference between collaborative and cooperative situations. 4.1. Division of task Division of labour is used by some authors to differentiate cooperation from collaboration. Cooperation occurs where different work is undertaken by different persons; Collaboration involves mutual engagement on work activities. Some researchers see these as mutually exclusive. If work is cooperative, a joint task is divided among participants, with each participant responsible for a portion of work. There is no element of mutual engagement on any subtask. For Roschelle and Teasley (1995) and Dillenbourg et al. (1996), the task is split hierarchically into independent subtasks. They contrast collaboration as an activity of coordinated, mutual engagement in problem solving, in which there is no separate division of joint task. For further elaboration of the literature following this perspective see Strijbos et al. (2004). However, for other researchers the division of labour is not a dichotomous term, as collaborative work may have an element of separation of subtasks. Therefore, division of labour is not a distinctive attribute of cooperation as it may occur under collaboration. Brna (1998) stresses that under collaboration, activities may be divided, with each party having its own goal and subtask; the essential characteristic of collaboration is a mutually agreed structure. Miyake (1986) also refers to this point in explaining that division of labour cannot be seen only in cooperation. In proposing a model of collaboration that contains cognitive, social, and environmental attributes, Harvey and Koubek (2000) also include division of tasks and coordination as characteristics of collaboration. Raposo et al. (2001) define collaboration towards a common goal as a coordinated set of tasks undertaken by group members. In their extensive study of collaboration, Wood and Gray (1991) refer to division of tasks between autonomous stakeholders. Mutual engagement in collaboration is the sharing of authority and acceptance of responsibility among group members (Panitz 1997). Collaborative work is a class of group work, hence, joint tasks — either in additive, disjunctive, or conjunctive forms — need participants’ domain knowledge for better performance. This highlights individual abilities of group members. From the discussion, we infer that the division of labour for joint tasks is an attribute of collaboration as well as cooperation. It is not a unique characteristic of cooperation. In interorganizational scheduling when participants collaborate to achieve a common goal and are

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mutually interested and engaged in collective work, they divide joint tasks and then apply their own domain knowledge to each subtask. 4.2. Coordination Coordination is the factor found to be a distinguishing characteristic of collaborative work in the literature. Participants acting collaboratively coordinate their efforts towards a common goal. From the perspective of a dichotomous classification, there is no coordination where work is cooperative. Coordination is required only for assembling the partial results of the divided joint task (Dillenbourg et al., 1996; Roschelle and Teasley, 1995). Using a framework of cooperative contextual work, Mentzas (1993) contends that each party’s behaviour must be somewhat predictable by the other parties for smooth cooperation. His description of the predictable process, that includes use in communication and operation, is equivalent to coordination. All cooperative activities must be somewhat coordinated. Malone and his colleagues at the MIT Centre for Coordination Science have comprehensively studied coordination. Their early definition of cooperation was “the act of working together harmoniously” (Malone and Crowston, 1990). By the time they completed their study, a decade later, they ameliorated their definition of coordination as the management of dependencies among activities (Malone and Crowston, 1994; Malone et al., 1999). They address three kinds of dependency that occur among activities: flow, fit, and sharing. Flow dependency happens when one activity produces a resource used by another activity. The second form is fit dependency that occurs in collective production of one resource by multiple activities. Sharing dependency pertains to situations in which multiple activities use the same resource. In defining generic forms of coordination, Castelfranchi (1998) also refers to the management of dependencies. According to him, “dependence is a special and strong case of interference”. Hoc stresses that coordination is an instrumental part of cooperation: “… coordination is an important component of cooperative activities.” However, “… cooperation cannot be reduced to coordination and other types of activity must be defined” (Hoc, 2001). That is, coordination is part of cooperative activities and cooperation consists of interference management. In summary, division of labour and coordination cannot be used as differentiating attributes for defining cooperation and collaboration. Both cooperative and collaborative activities can contain division of labour as well as coordination in joint tasks. Coordination is the management of dependencies among activities. Dependencies are taken as a special kind of interferences. So, managing dependencies (coordination) is a part of a wider activity, the management of interferences. By considering cooperation as management of interferences, as defined by Hoc (2001), coordination is a component of cooperation. 5. COLLABORATION STATE AND COOPERATIVE PROCESS Representations of collective work which use cooperation and collaboration, as mutually exclusive concepts are not useful for inter-organizational scheduling. Cooperation is a necessary but insufficient condition for describing such scheduling. Factors affecting participants’ behaviour cannot be reduced solely to cooperative work. Inter-organizational scheduling is a form of collaborative state. Collaboration is used here as a state of being — a philosophical term that may include temporal processes which is unlike the normally ascribed meaning of ‘state’ in engineering, which is associated with a set of attributes describing the condition of a system at an explicit time. Collaboration is a philosophy of interaction (Panitz, 1997): The state of being of the participants is one of collaboration. As a state, it has numbers of components: One of which is cooperation. The Venn diagram in Figure 1(a), components of a collaborative state, depicts this relationship. Participants in a collaborative state have to cooperate with each other to

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achieve mutually agreed goal(s). To collaborate with each other, participants must satisfy a set of conditions defining the collaborative state. For Brna (1998), collaboration as a state has the following five characteristics: • Mutually agreement to collaborate • Maintaining model of each others • Having a shared goal • Holding beliefs about the shared goal • Maintaining shared understanding of the problem Collaboration is a holistic view of the form of interaction, whereas cooperation is the process of managing interferences (Hoc, 2001). It occurs in many situations and is not restricted to collaborative states. However, coordination — the management of dependencies — is a component of cooperation (see the previous section). Managing interferences and dependencies involves a set of processes. For cooperation there is no need for a common goal to play a regulation role (Hoc, 2001). Cooperative activities may not even involve a common purpose (Hoc, 2001). Nonetheless, collaboration comprises both common goals and tasks. So, it can be seen that a collaborative scenario is but one of the scenarios in which cooperation occurs (see Figure 1(b)). Two or more parties may cooperate with each other without forming a collaborative relationship. That is, they cooperate without other conditions for a collaborative state being satisfied such as: mutually agreed shared goals, which define group tasks; parties forming appropriate mental models of each other’s behaviour; formation of group knowledge; participants striving together towards group goals, while working together with different roles.

(a)

(b)

Figure 1 (a) Cooperation as a component of collaborative state (b) Collaborative scenario as a case of cooperative work.

6. INTER-ORGANIZATIONAL SCHEDULING AS COLLABORATIVE WORK Collaboration is necessary for scheduling across organizational boundaries. It involves agents, human and perhaps computer, working together towards mutually agreed goals. It comprises cooperative activities, negotiation protocols and, possibly, combinatorial optimization solutions. However, more components exist in this collaborative state, especially as human experts play a key role in scheduling functions. Participants in such a work environment share group tasks. Having some form of mental model of each other’s primary role, they strive as a group towards the group goals, by applying group knowledge, albeit from the perspective of each participant’s role. We stress the importance of using the collaborative state in studying inter-organizational scheduling as a whole system. Researchers in many fields have studied different aspects of inter-organizational scheduling. Chan et al. (1999) propose a model for implementing a groupware application for distributed scheduling. Their work concerns web-based computer supported decision coordination. Individual databases are used to maintain each member’s information, while

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shared information is constructed by combining related components from each individual database with acknowledgment of their privacy. Their inter-organizational scheduling model includes two essential functions: view control and change control. The first function is to control users’ schemas of the project’s master plan, and the second one is the core of the negotiation process, where members cooperate and communicate to initiate, negotiate, and confirm schedules. For doing this, coordination part of the system uses predefined dependencies and rules to manage negotiation procedures. However, the authors refer to the inadequacy of these rules in complicated contextual scheduling scenarios and hence, they introduce a human coordinator with highest command for novel problem-solving. As a part of information model for building design, Mokhtar et al. (2000) use a collaborative planning and scheduling methodology to handle design changes. This method aims to find interdisciplinary design changes in case of probable amendment, finding alternative ways, estimating related cost, time, and labour, and level of recommendation of the ordered change. For this purpose, they introduce four stages in their collaborative scheduling module: Data collecting, organizing changes, scheduling changes, and calculations. In doing these steps, the method depends on a rule-based component in the information model, which is responsible to find corresponding parts of the building to a design change. This rule base of the model might not be able to solve the problems in dynamic and uncertain scheduling. Additionally, the system produces a set of feasible alternatives, for which final decision making remains on the human’s responsibility. Our interest lies predominantly in scheduling processes that incorporate considerable direct input by human decision-makers. Nevertheless, research into systems that exclusively use software agents can provide generic insights into relevant combinatorial-optimisation and system-architecture issues. Where the scheduling problem can be viewed as difficult from a mathematical perspective, much of the literature is concerned with relatively simple scenarios. Fink (2004) addresses two-party cooperation in a supply chain where each party is faced with a job sequencing problem. Fink finds that pairs of “cooperating” agents yield better results (i.e. objective-function values) for each of the firms they represent when compared to pairs of “greedy” agents. We argue that this particular result could be due largely to “cooperating” agents using probabilistic proposal-acceptance strategies that are akin to simulated-annealing (and which therefore allow a more thorough search of the solution space). However, a stronger result is that the utilisation of a pair of “cooperating” agents results in more balanced benefit-sharing amongst the partners, even though a shared goal is not explicitly stated within the scheduling system. We believe that this observation, although rather preliminary, has relevance to both human- and software- mediated scheduling processes. Fink also observes that “simple bargaining concepts or auction-based mechanisms are not suitable” in cooperation or collaboration scenarios with multiple interdependent issues (of which scheduling is a prime example). This is a view supported by the work of other authors including Klein et al. (2003) and Kowalczyk et al. (2004), who look at multi-attribute contract negotiation and multi-party task outsourcing respectively. It leads to the general (but not universal) acceptance that complex negotiation protocols and rich information exchange (e.g. the sharing of information about constraints) are necessary aspects of successful automated schedule coordination. This mirrors conclusions that can be drawn in relation to human-centred inter-organizational scheduling processes. In order to understand and model human aspects of inter-organizational scheduling, Nezamirad et al. (2004) construct a collaborative scheduling model. This model is intended to define a model relevant to human experts in collaboration environments in which participant organizations try to establish joint schedules. This schedules contain allocation of resources (both individual and common resource if exist) to the each participant’s tasks which are

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defined based on group tasks, group roles, and individual goals. A brief explanation of model’s component is discussed in following. 6.1. Individual and group goals Each participating organization has its own goals. These goals are initiatives for each participant to joint the collaboration. Individual goals influence group goals. Collaborators seeking mutual benefits orient their goals toward a ‘common aim’ that brings them together. This common aim is a base for establishing group goals which are common for all collaborators. However, having set up the group goals does not mean that all participants have the same individual goals. Human inter-organizational schedulers form a collaborative scheduling group to create ‘common goals’ based on common aim and ‘individual goals’. Hence, each scheduler has enough knowledge about his/her own organizational goals as well as common goals in the collaborative group. 6.2. Individual and group tasks Once group goals are established, ‘group tasks’ are defined in order to ensure that group goals are achieved. Group tasks are compound entities, which are carried out by related individuals. Therefore, group tasks are important source of ‘individual tasks’. Group tasks can be either in the form of additive, disjunctive, or conjunctive (Klein and Kozlowski, 2000). In an additive scheduling group task, all participants do the same job and group performance is a sum of individual performances. Higher abstraction scheduling activities are generally of this form. In disjunctive scheduling, group performance is disproportionately dependent on the strongest member. Problem-solving in novel scheduling situations is an example of this type. Conjunctive scheduling tasks comprise situations in which schedulers perform different but related tasks and group performance is disproportionately dependent on the weakest member. Lower-abstract activities in multi-party project scheduling are of this form. 6.3. Group roles Associated with the group tasks, each participant has a role. This role is a characteristic of the group task and differs for schedulers in different tasks. The structure of the group work is one important factor that influences group roles. For example, as Nezamirad et al. (2004) observe, hierarchical and heterarchical relation group-work structure brings different roles for schedulers. ‘Coordinator’ is one instance of group roles and in a case study presented by Nezamirad et al. (2004), a high-level steering committee takes the role of coordinator in collaborative scheduling. 6.4. Domain and group knowledge As mentioned before, scheduling problems deal with contextual information that might not be easily applied in computerized decision supports. Human schedulers joining collaborative environments are responsible for their own organization’s schedules. They use ‘domain knowledge’ including organizational policies, capacities, limitations, and heuristics. However, they use and share their domain knowledge in doing group. The shared knowledge is called ‘group knowledge’. Schedulers pool cognitive resources to enhance group’s performance. This shared information has been addressed by researchers from diverse range of expertise working on collaboration environments. Krauss and Fussell (1990) refer to common knowledge between group members and suggest three mechanisms to acquire it. Dix (1994) stresses the importance of establishing a mutual understanding in effective communication. Salas et al. (1995) address shared situation awareness for this issue. Clark and Brennan (1991) refer to communication as the process of establishing common ground whereas Harvey and Koubek (1998) see setting up a vocabulary schema during engineering collaboration. Hoc’s common frame of reference is constructed at action, plan, and meta levels and is a

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shared space between cooperative parties. In fact, collaborative schedulers, bringing their own domain contextual knowledge, make the group knowledge that comprises common schedules, organizational information, policies, historical records, glossaries, facilities and limitation etc that are useful to group members. 6.5. Group decision making process Scheduling decision-making and problem-solving processes can be accomplished in three generic phases using Miller (1988)’s three-step model of conceptualisation, visualisation, and realisation in collaborative manufacturing. In inter-organizational scheduling, participating schedulers first recognize the boundaries of the problem and the activities that are to be scheduled. Macro-level processes and high-level abstraction functions are considered in this stage. The next step is to visualising first stage’s concepts by applying documented characteristics. Here scheduling sketches are transferred between different levels of the collaborative organizations mainly in a debative form. Finalised schedules including task definitions, resources allocation, and timing are made at the last stage. Here schedulers are concerned with the formation of physical products. 6.6. Cooperation As the process of managing interferences, cooperative activities in collaborative scheduling situations are classified according to their level, form, and structure as outlined in section three. 7. IMPLICATIONS OF THE DISCUSSION AND CONCLUDING REMARKS This paper is a study of collective work in inter-organizational. It tackles the debate of “cooperation versus collaboration” by addressing two major and distinct issues: division of labour and coordination. These characteristics are not unique associated with either cooperation or collaboration. The discussion led to a new model of collaboration that defines collaboration as a state, which includes cooperation processes as one component. The utility of the model in analysing collective works in inter-organizational scheduling was then discussed. We stress that inter-organizational scheduling does not include only cooperation processes, as the form of the inter-relationships between parties is collaborative. This led to a model of collaborative scheduling, which has been presented by Nezamirad et al. (2004). By using the model, components of collective work are not missed during an analysis of interorganizational scheduling. The implications of this discussion can be demonstrated using two examples. In one study, Chan et al. (1999) introduce a collaborative scheduling system for coordinating work schedules in construction industry. They use cooperation and collaboration terms separately, as crucial factors in inter-organizational scheduling, but do not provide any definitions. Accordingly, their system works as a telephone exchange (their expression) to pass information and coordinated database for sharing data. They introduce a dependency intelligent list and a mechanism for resolving concurrency problems, as collaborative features of the system. Cooperation and collaboration in their system can be interpreted as coordinated negotiations that are triggered by user intervention. As a collaborative state is neither considered nor defined, forms of interaction before each party’s intervention – like anticipation of other parties’ plan – are not considered in this model. For instance, building designer, pre-caster and contractor cannot anticipate any intervention by their colleagues before it happens. As a result, every event has to be initiated, coordinated and refereed by the system, even if it could have been resolved (or debarred) by anticipation with less effort. In another work, Mokhtar et al. (2000) use a rule-based information model for collaborative scheduling of design changes. By not explicitly defining collaboration, their

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model only shows processes when they occur. For example, with this model a process for change amendment is instantiated only when it is announced by one of the parties. They apply the concepts of collaborative planning and scheduling to provide answers to questions regarding changes (i.e., time, labour, alternative ways, and cost of the design changes). By not including an explicit collaborative state, they ignore the effects of parties having shared models of each other (i.e., the different disciplines in the construction industry). They ignore other forms of interaction that are present in cooperation, when cooperation is defined as interference management. In contrast, a model that includes state would highlight collaborative relationships before any process occurs. To support mutual control over group task (design of the building) and anticipative planning, they should have included collaborative-planning models of the various types of personnel (e.g., architects, lighting designers and interior designers). This view to the state of collaboration can provide a means for the modelling, analysis, evaluation, and design of scheduling systems between organizations. The primary contribution of the proposed approach is in analysis and modelling human aspects that affect inter-organizational scheduling. It attempts to identify the attributes and their interactions that influence humans performing collective tasks in collaborative states. By decomposing collaborative environment to its components, this approach is useful in diagnosis and evaluation of such systems. It provides a framework to evaluate the translation of real situations within the problem space and computer decision supports or to evaluate internal and external organizational effects of such systems. As an example, it can be seen systems that include negotiation protocols and mechanisms should refer to other components of the collaborative model. It is useful also for benchmarking, where comparing different systems can be facilitated using the diagnosis and evaluation tools. Another important usage of this approach is in design of structures and computer supports for collaborative work in scheduling. It can be used to determine whether, how much, and which structure is necessary with respect to scheduling objectives, expected interaction, and tasks’ type. It is also useful to plan necessary organizational changes when working as a group in a collaborative state. Consideration of inter-organizational scheduling as a collaborative state should be a major part of designing software supports and interfaces. Computer-supported tools for scheduling between organizations, should involve sharing cognitive as well as technological resources. They also should provide means for information sharing and making common group knowledge. As a basis of our model, the division of labour is a part of collaborative work and there should be mechanisms for it in software supports. This is the same for coordination issues and other components of the model. To sum up, we would suggest that this approach is helpful in (re) designing structures for scheduling between organizations, can bring in guidelines for identifying cooperative activities, negotiation processes and their relationship, and highlights the basic concepts for designing computer supports for inter-organizational scheduling. The idea of a Collaborative state, containing different aspects of the problem domain, should be a major part of the “thinking process” in system analysts, designers, software developers, and other affecting authorities in scheduling between organizations. REFERENCES Brna, P. (1998), Models of Collaboration, Proceedings of the Workshop on Informatics in Education, XVIII Congresso Nacional da Sociedade Brasileira de Computaçăo Rumoa Sociedade do Conhecimento, Belo Horizone, Brazil. Buzacott, J. A. (2004), Modelling Teams and Workgroups in Manufacturing, Annals of Operations Research, 126, 215-30.

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